The present invention relates to a discerning method applied to a character/graphics recognition apparatus, particularly for the purpose of effectively discerning whether character/graphics patterns are acceptable or not.
When character/graphic image printed on an electronic component or the like is recognized and discerned, the geometric feature of the a character/graphic image is extracted from the data obtained by reading and quantizing the character/graphic image. As one example of a discerning method, a description with reference to FIGS. 4 and 5 will be made in which a printed character "T" is to be discerned. In the drawings, a character area is denoted by oblique lines. In FIG. 4, the character is an acceptable character 5. The character in FIG. 5 is an unacceptable character 6 having a blur 6a. For discerning the blur 6a as unacceptable, the image data from an image pick-up device such as a camera is turned to binary digits by an image binary-coding means and then, the obtained binary image is sent to a calculating means, where a shape feature value is calculated. Whether the printed character "T" as an object to be detected is acceptable or unacceptable is discerned from the resultant data. This discerning method uses a plurality of feature values, and in particular, generally nine kinds of feature values are used as follows:
[1] area of a character part PA1 [2] peripheral length of the character part PA1 [3] frame length of the character part PA1 [4] peripheral angle distribution of the character part PA1 [5] frame angle distribution of the character part PA1 [6] projective length of the character part relative to an X axis PA1 [7] projective length of the character part relative to a Y axis PA1 [8] secondary moment around X' axis passing the center of gravity of the character part PA1 [9] secondary moment around Y' axis passing the center of gravity of the character part
Each of the above feature values will be depicted more in detail.
The area of a character part [1] represents the number of pixels occupying the character part. The pixel is a unit of image information. The peripheral length of the character part [2] is the length of the periphery of the character "T" indicated by a solid line 7 in FIG. 6 when the length of each pixel is rendered 1. The frame length of the character part [3] is the length of a segment of the frame of the character "T" which is drawn by a solid line 9 within a broken line 8 in FIG. 7 while the length of each pixel is 1. Moreover, the peripheral angle distribution of the character part [4] and the frame angle distribution of the character part [5] indicate the distribution of the peripheral length and frame length of the character part, respectively, obtained by summing the connecting number of times when the two adjacent pixels A, A are connected in transverse, vertical, right slantwise and left slantwise directions, as shown in FIGS. 8(a)-8(d). The projective length of the character part relative to the X axis [6] and the projective length of the character part relative to the Y axis [7] represent the length of a straight line connecting a segment in the X axis direction and in the Y axis direction of the character part with a point outside the character part, respectively. The secondary moment around the X' axis passing the center of gravity of the character part [8] is a mean of a square of the difference between the center of gravity G and the segment in the X' axis direction in FIG. 4, which represents the variance to the X' axis direction. Meanwhile, the secondary moment around the Y' axis passing the center of gravity of the character part [9] is a mean of a square of the difference between the center of gravity G and the segment in the Y' axis direction of FIG. 4, indicative of the variance to the Y' axis direction.
When the character pattern "T" is to be discerned with use of each feature value as above, although there is little difference in the feature value of the area between the acceptable character 5 in FIG. 4 and the unacceptable character 6 having the blur 6a in FIG. 5, the peripheral length of the acceptable character 5 is smaller than that of the unacceptable character 6. Therefore, it is possible to roughly discern the characters 5 and 6 by referring to the peripheral length thereof, which would be hardly achieved with reference only to the feature value of the area. Since this fact holds true for the other kinds of the feature values, it is necessary to make a decision after calculating the each kind of feature value.
However, in the discerning method described above, a quantitative reference is not set in many cases, and therefore it is uncertain which of the feature values is to be used for proper discerning. That is, calculations for many kinds of the feature values should be repeated until the difference between the acceptable object 5 and unacceptable object 6 becomes clearly identified. As such, the calculation results in wasteful consumption of time, requiring a computer of a large capacity. Although a neural network system has been employed to discern the object of this kind in the past years, the layered circuit structure of the neural network system is complicated, and therefore, the learning time through back propagation is elongated as the number of the kinds of the feature values fed to an image input means is increased. In some cases, it is difficult for the neutral network to converge on a conclusion particularly when too much information is input, and processing is possibly disabled.